使用飞桨PaddleHub实现将视频动作转化为皮影戏
作者：互联网
前言
飞桨(PaddlePaddle)是集深度学习核心框架、工具组件和服务平台为一体的技术先进、功能完备的开源深度学习平台，已被中国企业广泛使用，深度契合企业应用需求，拥有活跃的开发者社区生态。提供丰富的官方支持模型集合，我们这里将要使用到其中的骨骼节点检测模型，通过PaddleHub提供的人体骨骼关键点检测预训练模型，我们就可以快速实现皮影戏的效果。
这里说一下这个项目的大体实现流程，先将现有的视频按帧剪切为一张张的图片，并保存到本地，使用PaddleHub提供的人体骨骼关键点检测预训练模型来获取每张图片里人物作出动作时的骨骼关键节点模型，例如左手、左脚、右手、右脚、躯干以及头颅的位置以及方向，在PaddleHub获取到人体骨骼关键点模型之后，就可以对这些关键点进行连接，从而形成了人体姿态。接着我们将皮影的身体躯干素材拼接到模型上，这就完成了将图片里的人物动作转化为皮影戏。
将每张图片都这样操作，保存拼接之后的的图片，将所有的图片合成为视频即可让皮影跟随人体姿态进行运动，就达到“皮影戏”的效果。
皮影素材
项目实现过程中使用的Python版本为3.7.0，其他依赖库的版本分别为cv2 4.5.1.48、matplotlib 2.2.2、numpy 1.19.3、tensorflow 2.4.1。
具体流程
一、安装依赖库以及模型
安装PaddlePaddle
windows cpu版本快速安装
python -m pip install paddlepaddle==2.0.2 -i https://mirror.baidu.com/pypi/simple
其他版本安装请参考官网https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/windows-pip.html
安装PaddleHub
pip install PaddleHub
导入人体骨骼关键节点检测模型
hub install human_pose_estimation_resnet50_mpii==1.1.1
二、检测是否安装成功
检测图片骨骼节点
import os
import cv2
import paddlehub as hub
import matplotlib.pyplot as plt
from matplotlib.image import imread
import numpy as np
def show_img(img_path, size=8):
'''
文件读取图片显示
'''
im = imread(img_path)
plt.figure(figsize=(size, size))
plt.axis("off")
plt.imshow(im)
def img_show_bgr(image, size=8):
'''
cv读取的图片显示
'''
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(size, size))
plt.imshow(image)
plt.axis("off")
plt.show()
pose_estimation = hub.Module(name="human_pose_estimation_resnet50_mpii")
result = pose_estimation.keypoint_detection(paths=['test4.jpg'], visualization=True, output_dir="work/output_pose/")
print(result)
拼接皮影素材
这一步需要用到皮影戏的素材，请移步到文末下载
import os
import cv2
import paddlehub as hub
import matplotlib.pyplot as plt
from matplotlib.image import imread
import numpy as np
def show_img(img_path, size=8):
'''
文件读取图片显示
'''
im = imread(img_path)
plt.figure(figsize=(size,size))
plt.axis("off")
plt.imshow(im)
def img_show_bgr(image,size=8):
'''
cv读取的图片显示
'''
image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
plt.figure(figsize=(size,size))
plt.imshow(image)
plt.axis("off")
plt.show()
show_img('work/imgs/body01.jpg')
def get_true_angel(value):
'''
转转得到角度值
'''
return value/np.pi*180
def get_angle(x1, y1, x2, y2):
'''
计算旋转角度
'''
dx = abs(x1- x2)
dy = abs(y1- y2)
result_angele = 0
if x1 == x2:
if y1 > y2:
result_angele = 180
else:
if y1!=y2:
the_angle = int(get_true_angel(np.arctan(dx/dy)))
if x1 < x2:
if y1>y2:
result_angele = -(180 - the_angle)
elif y1<y2:
result_angele = -the_angle
elif y1==y2:
result_angele = -90
elif x1 > x2:
if y1>y2:
result_angele = 180 - the_angle
elif y1<y2:
result_angele = the_angle
elif y1==y2:
result_angele = 90
if result_angele<0:
result_angele = 360 + result_angele
return result_angele
def rotate_bound(image, angle, key_point_y):
'''
旋转图像，并取得关节点偏移量
'''
#获取图像的尺寸
(h,w) = image.shape[:2]
#旋转中心
(cx,cy) = (w/2,h/2)
# 关键点必须在中心的y轴上
(kx,ky) = cx, key_point_y
d = abs(ky - cy)
#设置旋转矩阵
M = cv2.getRotationMatrix2D((cx,cy), -angle, 1.0)
cos = np.abs(M[0,0])
sin = np.abs(M[0,1])
# 计算图像旋转后的新边界
nW = int((h*sin)+(w*cos))
nH = int((h*cos)+(w*sin))
# 计算旋转后的相对位移
move_x = nW/2 + np.sin(angle/180*np.pi)*d
move_y = nH/2 - np.cos(angle/180*np.pi)*d
# 调整旋转矩阵的移动距离（t_{x}, t_{y}）
M[0,2] += (nW/2) - cx
M[1,2] += (nH/2) - cy
return cv2.warpAffine(image,M,(nW,nH)), int(move_x), int(move_y)
def get_distences(x1, y1, x2, y2):
return ((x1-x2)**2 + (y1-y2)**2)**0.5
def append_img_by_sk_points(img, append_img_path, key_point_y, first_point, second_point, append_img_reset_width=None,
append_img_max_height_rate=1, middle_flip=False, append_img_max_height=None):
'''
将需要添加的肢体图片进行缩放
'''
append_image = cv2.imdecode(np.fromfile(append_img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
# 根据长度进行缩放
sk_height = int(get_distences(first_point[0], first_point[1], second_point[0], second_point[1])*append_img_max_height_rate)
# 缩放制约
if append_img_max_height:
sk_height = min(sk_height, append_img_max_height)
sk_width = int(sk_height/append_image.shape[0]*append_image.shape[1]) if append_img_reset_width is None else int(append_img_reset_width)
if sk_width <= 0:
sk_width = 1
if sk_height <= 0:
sk_height = 1
# 关键点映射
key_point_y_new = int(key_point_y/append_image.shape[0]*append_image.shape[1])
# 缩放图片
append_image = cv2.resize(append_image, (sk_width, sk_height))
img_height, img_width, _ = img.shape
# 是否根据骨骼节点位置在 图像中间的左右来控制是否进行 左右翻转图片
# 主要处理头部的翻转, 默认头部是朝左
if middle_flip:
middle_x = int(img_width/2)
if first_point[0] < middle_x and second_point[0] < middle_x:
append_image = cv2.flip(append_image, 1)
# 旋转角度
angle = get_angle(first_point[0], first_point[1], second_point[0], second_point[1])
append_image, move_x, move_y = rotate_bound(append_image, angle=angle, key_point_y=key_point_y_new)
app_img_height, app_img_width, _ = append_image.shape
zero_x = first_point[0] - move_x
zero_y = first_point[1] - move_y
(b, g, r) = cv2.split(append_image)
for i in range(0, r.shape[0]):
for j in range(0, r.shape[1]):
if 230>r[i][j]>200 and 0<=zero_y+i<img_height and 0<=zero_x+j<img_width:
img[zero_y+i][zero_x+j] = append_image[i][j]
return img
body_img_path_map = {
"right_hip" : "./work/shadow_play_material/right_hip.jpg",
"right_knee" : "./work/shadow_play_material/right_knee.jpg",
"left_hip" : "./work/shadow_play_material/left_hip.jpg",
"left_knee" : "./work/shadow_play_material/left_knee.jpg",
"left_elbow" : "./work/shadow_play_material/left_elbow.jpg",
"left_wrist" : "./work/shadow_play_material/left_wrist.jpg",
"right_elbow" : "./work/shadow_play_material/right_elbow.jpg",
"right_wrist" : "./work/shadow_play_material/right_wrist.jpg",
"head" : "./work/shadow_play_material/head.jpg",
"body" : "./work/shadow_play_material/body.jpg"
}
def get_combine_img(img_path, pose_estimation=pose_estimation, body_img_path_map=body_img_path_map, backgroup_img_path= 'work/background.jpg'):
'''
识别图片中的关节点，并将皮影的肢体进行对应，最后与原图像拼接后输出
'''
result = pose_estimation.keypoint_detection(paths=[img_path])
image=cv2.imread(img_path)
# 背景图片
backgroup_image = cv2.imread(backgroup_img_path)
image_flag = cv2.resize(backgroup_image, (image.shape[1], image.shape[0]))
# 最小宽度
min_width = int(get_distences(result[0]['data']['head_top'][0], result[0]['data']['head_top'][1],
result[0]['data']['upper_neck'][0], result[0]['data']['upper_neck'][1])/3)
#右大腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0], result[0]['data']['right_hip'][1])*1.6), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_hip'], key_point_y=10, first_point=result[0]['data']['right_hip'],
second_point=result[0]['data']['right_knee'], append_img_reset_width=append_img_reset_width)
# 右小腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0], result[0]['data']['right_hip'][1])*1.5), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_knee'], key_point_y=10, first_point=result[0]['data']['right_knee'],
second_point=result[0]['data']['right_ankle'], append_img_reset_width=append_img_reset_width)
# 左大腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0], result[0]['data']['left_hip'][1])*1.6), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_hip'], key_point_y=0, first_point=result[0]['data']['left_hip'],
second_point=result[0]['data']['left_knee'], append_img_reset_width=append_img_reset_width)
# 左小腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0], result[0]['data']['left_hip'][1])*1.5), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_knee'], key_point_y=10, first_point=result[0]['data']['left_knee'],
second_point=result[0]['data']['left_ankle'], append_img_reset_width=append_img_reset_width)
# 右手臂
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_elbow'], key_point_y=25, first_point=result[0]['data']['right_shoulder'],
second_point=result[0]['data']['right_elbow'], append_img_max_height_rate=1.2)
# 右手肘
append_img_max_height = int(get_distences(result[0]['data']['right_shoulder'][0], result[0]['data']['right_shoulder'][1],
result[0]['data']['right_elbow'][0], result[0]['data']['right_elbow'][1])*1.6)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_wrist'], key_point_y=10, first_point=result[0]['data']['right_elbow'],
second_point=result[0]['data']['right_wrist'], append_img_max_height_rate=1.5,
append_img_max_height=append_img_max_height)
# 左手臂
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_elbow'], key_point_y=25, first_point=result[0]['data']['left_shoulder'],
second_point=result[0]['data']['left_elbow'], append_img_max_height_rate=1.2)
# 左手肘
append_img_max_height = int(get_distences(result[0]['data']['left_shoulder'][0], result[0]['data']['left_shoulder'][1],
result[0]['data']['left_elbow'][0], result[0]['data']['left_elbow'][1])*1.6)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_wrist'], key_point_y=10, first_point=result[0]['data']['left_elbow'],
second_point=result[0]['data']['left_wrist'], append_img_max_height_rate=1.5,
append_img_max_height=append_img_max_height)
# 头
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['head'], key_point_y=10, first_point=result[0]['data']['head_top'],
second_point=result[0]['data']['upper_neck'], append_img_max_height_rate=1.2, middle_flip=True)
# 身体
append_img_reset_width = max(int(get_distences(result[0]['data']['left_shoulder'][0], result[0]['data']['left_shoulder'][1],
result[0]['data']['right_shoulder'][0], result[0]['data']['right_shoulder'][1])*1.2), min_width*3)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['body'], key_point_y=20, first_point=result[0]['data']['upper_neck'],
second_point=result[0]['data']['pelvis'], append_img_reset_width=append_img_reset_width, append_img_max_height_rate=1.2)
result_img = np.concatenate((image, image_flag), axis=1)
return result_img
pos_img_path = 'work/output_pose/body01.jpg'
result_img = get_combine_img(pos_img_path, pose_estimation, body_img_path_map)
img_show_bgr(result_img, size=10)
三、让皮影动起来
准备一个含有人体动作视频，视频素材可以到b站舞蹈区进行下载，将代码中的路径更改为相应的视频路径，并新建mp4_img、mp4_img_analysis文件夹存储相关文件。
这一步耗时比较长，主要看你的电脑配置，我用的笔记本i5+MX150独显配置，两分钟的视频跑了一个半小时才跑完。
import os
import cv2
import paddlehub as hub
import matplotlib.pyplot as plt
from matplotlib.image import imread
import numpy as np
def show_img(img_path, size=8):
'''
文件读取图片显示
'''
im = imread(img_path)
plt.figure(figsize=(size, size))
plt.axis("off")
plt.imshow(im)
def img_show_bgr(image, size=8):
'''
cv读取的图片显示
'''
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(size, size))
plt.imshow(image)
plt.axis("off")
plt.show()
pose_estimation = hub.Module(name="human_pose_estimation_resnet50_mpii")
result = pose_estimation.keypoint_detection(paths=['test4.jpg'], visualization=True, output_dir="work/output_pose/")
print(result)
def get_true_angel(value):
'''
转转得到角度值
'''
return value / np.pi * 180
def get_angle(x1, y1, x2, y2):
'''
计算旋转角度
'''
dx = abs(x1 - x2)
dy = abs(y1 - y2)
result_angele = 0
if x1 == x2:
if y1 > y2:
result_angele = 180
else:
if y1 != y2:
the_angle = int(get_true_angel(np.arctan(dx / dy)))
if x1 < x2:
if y1 > y2:
result_angele = -(180 - the_angle)
elif y1 < y2:
result_angele = -the_angle
elif y1 == y2:
result_angele = -90
elif x1 > x2:
if y1 > y2:
result_angele = 180 - the_angle
elif y1 < y2:
result_angele = the_angle
elif y1 == y2:
result_angele = 90
if result_angele < 0:
result_angele = 360 + result_angele
return result_angele
def rotate_bound(image, angle, key_point_y):
'''
旋转图像，并取得关节点偏移量
'''
# 获取图像的尺寸
(h, w) = image.shape[:2]
# 旋转中心
(cx, cy) = (w / 2, h / 2)
# 关键点必须在中心的y轴上
(kx, ky) = cx, key_point_y
d = abs(ky - cy)
# 设置旋转矩阵
M = cv2.getRotationMatrix2D((cx, cy), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# 计算图像旋转后的新边界
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# 计算旋转后的相对位移
move_x = nW / 2 + np.sin(angle / 180 * np.pi) * d
move_y = nH / 2 - np.cos(angle / 180 * np.pi) * d
# 调整旋转矩阵的移动距离（t_{x}, t_{y}）
M[0, 2] += (nW / 2) - cx
M[1, 2] += (nH / 2) - cy
return cv2.warpAffine(image, M, (nW, nH)), int(move_x), int(move_y)
def get_distences(x1, y1, x2, y2):
return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
def append_img_by_sk_points(img, append_img_path, key_point_y, first_point, second_point, append_img_reset_width=None,
append_img_max_height_rate=1, middle_flip=False, append_img_max_height=None):
'''
将需要添加的肢体图片进行缩放
'''
append_image = cv2.imdecode(np.fromfile(append_img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
# 根据长度进行缩放
sk_height = int(
get_distences(first_point[0], first_point[1], second_point[0], second_point[1]) * append_img_max_height_rate)
# 缩放制约
if append_img_max_height:
sk_height = min(sk_height, append_img_max_height)
sk_width = int(
sk_height / append_image.shape[0] * append_image.shape[1]) if append_img_reset_width is None else int(
append_img_reset_width)
if sk_width <= 0:
sk_width = 1
if sk_height <= 0:
sk_height = 1
# 关键点映射
key_point_y_new = int(key_point_y / append_image.shape[0] * append_image.shape[1])
# 缩放图片
append_image = cv2.resize(append_image, (sk_width, sk_height))
img_height, img_width, _ = img.shape
# 是否根据骨骼节点位置在 图像中间的左右来控制是否进行 左右翻转图片
# 主要处理头部的翻转, 默认头部是朝左
if middle_flip:
middle_x = int(img_width / 2)
if first_point[0] < middle_x and second_point[0] < middle_x:
append_image = cv2.flip(append_image, 1)
# 旋转角度
angle = get_angle(first_point[0], first_point[1], second_point[0], second_point[1])
append_image, move_x, move_y = rotate_bound(append_image, angle=angle, key_point_y=key_point_y_new)
app_img_height, app_img_width, _ = append_image.shape
zero_x = first_point[0] - move_x
zero_y = first_point[1] - move_y
(b, g, r) = cv2.split(append_image)
for i in range(0, r.shape[0]):
for j in range(0, r.shape[1]):
if 230 > r[i][j] > 200 and 0 <= zero_y + i < img_height and 0 <= zero_x + j < img_width:
img[zero_y + i][zero_x + j] = append_image[i][j]
return img
body_img_path_map = {
"right_hip" : "./work/shadow_play_material/right_hip.jpg",
"right_knee" : "./work/shadow_play_material/right_knee.jpg",
"left_hip" : "./work/shadow_play_material/left_hip.jpg",
"left_knee" : "./work/shadow_play_material/left_knee.jpg",
"left_elbow" : "./work/shadow_play_material/left_elbow.jpg",
"left_wrist" : "./work/shadow_play_material/left_wrist.jpg",
"right_elbow" : "./work/shadow_play_material/right_elbow.jpg",
"right_wrist" : "./work/shadow_play_material/right_wrist.jpg",
"head" : "./work/shadow_play_material/head.jpg",
"body" : "./work/shadow_play_material/body.jpg"
}
def get_combine_img(img_path, pose_estimation=pose_estimation, body_img_path_map=body_img_path_map,
backgroup_img_path='background.jpg'):
'''
识别图片中的关节点，并将皮影的肢体进行对应，最后与原图像拼接后输出
'''
result = pose_estimation.keypoint_detection(paths=[img_path])
image = cv2.imread(img_path)
# 背景图片
backgroup_image = cv2.imread(backgroup_img_path)
image_flag = cv2.resize(backgroup_image, (image.shape[1], image.shape[0]))
# 最小宽度
min_width = int(get_distences(result[0]['data']['head_top'][0], result[0]['data']['head_top'][1],
result[0]['data']['upper_neck'][0], result[0]['data']['upper_neck'][1]) / 3)
# 右大腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['right_hip'][1]) * 1.6), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_hip'], key_point_y=10,
first_point=result[0]['data']['right_hip'],
second_point=result[0]['data']['right_knee'],
append_img_reset_width=append_img_reset_width)
# 右小腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['right_hip'][1]) * 1.5), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_knee'], key_point_y=10,
first_point=result[0]['data']['right_knee'],
second_point=result[0]['data']['right_ankle'],
append_img_reset_width=append_img_reset_width)
# 左大腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['left_hip'][1]) * 1.6), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_hip'], key_point_y=0,
first_point=result[0]['data']['left_hip'],
second_point=result[0]['data']['left_knee'],
append_img_reset_width=append_img_reset_width)
# 左小腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['left_hip'][1]) * 1.5), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_knee'], key_point_y=10,
first_point=result[0]['data']['left_knee'],
second_point=result[0]['data']['left_ankle'],
append_img_reset_width=append_img_reset_width)
# 右手臂
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_elbow'], key_point_y=25,
first_point=result[0]['data']['right_shoulder'],
second_point=result[0]['data']['right_elbow'], append_img_max_height_rate=1.2)
# 右手肘
append_img_max_height = int(
get_distences(result[0]['data']['right_shoulder'][0], result[0]['data']['right_shoulder'][1],
result[0]['data']['right_elbow'][0], result[0]['data']['right_elbow'][1]) * 1.6)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_wrist'], key_point_y=10,
first_point=result[0]['data']['right_elbow'],
second_point=result[0]['data']['right_wrist'], append_img_max_height_rate=1.5,
append_img_max_height=append_img_max_height)
# 左手臂
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_elbow'], key_point_y=25,
first_point=result[0]['data']['left_shoulder'],
second_point=result[0]['data']['left_elbow'], append_img_max_height_rate=1.2)
# 左手肘
append_img_max_height = int(
get_distences(result[0]['data']['left_shoulder'][0], result[0]['data']['left_shoulder'][1],
result[0]['data']['left_elbow'][0], result[0]['data']['left_elbow'][1]) * 1.6)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_wrist'], key_point_y=10,
first_point=result[0]['data']['left_elbow'],
second_point=result[0]['data']['left_wrist'], append_img_max_height_rate=1.5,
append_img_max_height=append_img_max_height)
# 头
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['head'], key_point_y=10,
first_point=result[0]['data']['head_top'],
second_point=result[0]['data']['upper_neck'], append_img_max_height_rate=1.2,
middle_flip=True)
# 身体
append_img_reset_width = max(
int(get_distences(result[0]['data']['left_shoulder'][0], result[0]['data']['left_shoulder'][1],
result[0]['data']['right_shoulder'][0], result[0]['data']['right_shoulder'][1]) * 1.2),
min_width * 3)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['body'], key_point_y=20,
first_point=result[0]['data']['upper_neck'],
second_point=result[0]['data']['pelvis'],
append_img_reset_width=append_img_reset_width, append_img_max_height_rate=1.2)
result_img = np.concatenate((image, image_flag), axis=1)
return result_img
##pos_img_path = 'test3.jpg'
##result_img = get_combine_img(pos_img_path, pose_estimation, body_img_path_map)
##img_show_bgr(result_img, size=10)
input_video = 'work/test.mp4'
def transform_video_to_image(video_file_path, img_path):
'''
将视频中每一帧保存成图片
'''
video_capture = cv2.VideoCapture(video_file_path)
fps = video_capture.get(cv2.CAP_PROP_FPS)
count = 0
while(True):
ret, frame = video_capture.read()
if ret:
cv2.imwrite(img_path + '%d.jpg' % count, frame)
count += 1
else:
break
video_capture.release()
print('视频图片保存成功, 共有 %d 张' % count)
return fps
fps = transform_video_to_image(input_video, 'work/mp4_img/')
def analysis_pose(input_frame_path, output_frame_path, is_print=True):
'''
分析图片中的人体姿势， 并转换为皮影姿势，输出结果
'''
file_items = os.listdir(input_frame_path)
file_len = len(file_items)
for i, file_item in enumerate(file_items):
if is_print:
print(i+1,'/', file_len, ' ', os.path.join(output_frame_path, file_item))
combine_img = get_combine_img(os.path.join(input_frame_path, file_item))
cv2.imwrite(os.path.join(output_frame_path, file_item), combine_img)
analysis_pose('work/mp4_img/', 'work/mp4_img_analysis/', is_print=False)
def combine_image_to_video(comb_path, output_file_path, fps=30, is_print=False):
'''
合并图像到视频
'''
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
file_items = os.listdir(comb_path)
file_len = len(file_items)
# print(comb_path, file_items)
if file_len > 0:
temp_img = cv2.imread(os.path.join(comb_path, file_items[0]))
img_height, img_width = temp_img.shape[0], temp_img.shape[1]
out = cv2.VideoWriter(output_file_path, fourcc, fps, (img_width, img_height))
for i in range(file_len):
pic_name = os.path.join(comb_path, str(i) + ".jpg")
if is_print:
print(i + 1, '/', file_len, ' ', pic_name)
img = cv2.imread(pic_name)
out.write(img)
out.release()
combine_image_to_video('work/mp4_img_analysis/', 'work/mp4_analysis.mp4', fps)
实现效果
实现效果视频链接极乐天堂皮影戏
标签：PaddleHub,img,image,飞桨,result,path,data,皮影戏,append 来源： https://blog.csdn.net/weixin_43913566/article/details/116454128